End-tidal carbon dioxide and arterial to end-tidal carbon dioxide gradient are associated with mortality in patients with neurological injuries.
Arthur Le GallGabriel EustacheAlice CoquetPhilippe SeguinYoann LauneyPublished in: Scientific reports (2024)
Pre-hospital end-tidal carbon dioxide (E t CO 2 ) monitoring and arterial to end-tidal carbon dioxide gradient (P a-Et CO 2 ) have been associated with mortality in patients with traumatic brain injury. Our study aimed to analyze the association between alveolar E t CO 2 or P a-Et CO 2 and mortality in patients admitted in intensive care unit (ICU) with neurological injuries. In our retrospective analysis from using large de-identified ICU databases (MIMIC-III and -IV and eICU databases), we included 2872 ICU patients with neurological injuries, identified according to the International Classification of Diseases (ICD-9 and -10), who underwent E t CO 2 monitoring. We performed logistic regression and extended Cox regression to assess the association between mortality and candidate covariates, including E t CO 2 and P a-Et CO 2 . In-hospital mortality was 26% (n = 747). In univariate analysis, both the P a-Et CO 2 gradient and E t CO 2 levels during the first 24 h were significantly associated with mortality (for a 1 mmHg increase: OR = 1.03 [CI 95 1.016-1.035] and OR = 0.94 [CI 95 0.923-0.953]; p < 0.001). The association remained significant in multivariate analysis. The time-varying evolution of EtCO 2 was independently associated with mortality (for a 1 mmHg increase: HR = 0.976 [CI 95 0.966-0.985]; p < 0.001). The time-varying P a-Et CO 2 gradient was associated with mortality only in univariate analysis. In neurocritical patients, lower E t CO 2 levels at admission and throughout the ICU stay were independently associated with mortality and should be avoided.
Keyphrases
- carbon dioxide
- intensive care unit
- cardiovascular events
- traumatic brain injury
- risk factors
- emergency department
- machine learning
- coronary artery disease
- newly diagnosed
- type diabetes
- deep learning
- big data
- patient reported outcomes
- prognostic factors
- blood brain barrier
- artificial intelligence
- subarachnoid hemorrhage